Distinct mechanisms underlie H2O2 sensing in C. elegans head and tail

Environmental oxidative stress threatens cellular integrity and should therefore be avoided by living organisms. Yet, relatively little is known about environmental oxidative stress perception. Here, using microfluidics, we showed that like I2 pharyngeal neurons, the tail phasmid PHA neurons function as oxidative stress sensing neurons in C. elegans, but display different responses to H2O2 and light. We uncovered that different but related receptors, GUR-3 and LITE-1, mediate H2O2 signaling in I2 and PHA neurons. Still, the peroxiredoxin PRDX-2 is essential for both, and might promote H2O2-mediated receptor activation. Our work demonstrates that C. elegans can sense a broad range of oxidative stressors using partially distinct H2O2 signaling pathways in head and tail sensillae, and paves the way for further understanding of how the integration of these inputs translates into the appropriate behavior.


Movie registration -> readworm_PHA ('movie_name.tif')
After ROI selection with the 2 neurons of interest (shown above), the function readworm_PHA processes every movie time point and performs the alignment of neurons, generating two files: movie-registered.mat movie.mp4 The mp4 allows the user to quickly verify that the movie has been correctly aligned. Note: supplemental movies 1-14 were generated using this program.

-> viewworm_2('movie_name')
The function opens a figure project with several images and buttons. The top left image allows movie traveling in the registered movie for each frame, using the right and left keyboard arrows, or typing directly the frame number in the box at the bottom of the window. To better visualize neurons, the intensity threshold of the image viewer can be adjusted with the top and bottom keyboard arrows.
Using the 'Pixel Train' button, the user teaches the program which pixels should be selected for neuronal segmentation by painting them in green (left mouse click). The pixels which should not be included for segmentation are painted in red (right mouse click). This needs to be done for several time points to obtain the best segmentation results, especially during the neuronal response. Below is shown an example with the selected neurons in green and the posterior neurite in red (excluded from the analysis), during the response.
When this teaching has been done as often as needed, the user clicks 'Classify pixels', leading to the neuron segmentation as defined by the user. Then the user defines each neuron in the bottom segmented image, by clicking the 'Select neurons' button: right click-> red selection (left neuron); left click -> blue neuron (right neuron); Upon pressing the 'Track' button, left and right neurons will be tracked for all movie time points. Upon pressing 'Plot', the mean fluorescence intensity is indicated as a function of time, generating the red and blue curves in the bottom right panel, as shown below.
Important note : this step allows the correction of tracking errors at every frame. The quality control is performed by the user by selecting the appropriate neuron and clicking 'Track' again until all tracking errors are corrected. Below is shown an example of error correction at time point 164 (pixels colored in green are excluded from the quantification in the bottom left box): When the movie is properly tracked to the end, click 'Save' button to obtain the .fig files which will be used for subsequent analyses. The user needs to indicate the T0 in the command window, which corresponds to the time point at which the stress/ stimulus was applied.
Finally click on 'Movie' to generate an mp4 movie showing the registered, segmented and tracked movies altogether, allowing an assessment of the global quality of the analysis.
Once all movies have been successfully processed, create a folder containing all the individual .fig analyzed files, which will be used for processing the average curve.

-> processAverage('low,high,offset')
This function will generate average curves for red and blue neurons, for raw and normalized data.
Finally to retrieve individual intensity mean values at the response peak, run the ExportSingleWormData function which will produce an Excel sheet with all individual peak values.

II-Analysis of single cell transcriptomic data in ASH and PHA neurons (for the generation of the dot plots in S13 Fig)
Single-cell RNA-sequencing data was generated by (Cao et al. 2017). Clusters and subclusters were defined as described in (Lorenzo et al. 2020), using Louvain clustering at 8 PCs and at 1.2 resolution. The dataset normalization was performed using Seurat v4 (Hao et al. 2021), and visualized using dot plots. We performed differential analyses using the 'FindMarkers' function of Seurat v4 to compare cluster 28 versus all neurons (data in S1 Table); cluster 28.0 (PHA) versus all neurons (data in S2 Table); clusters 28.1 (ASH) versus all neurons (data in S3 Table). Selected genes were chosen among the most significantly enriched from these lists.